| # from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| # download = False | |
| # save_model_locally= False | |
| # if download: | |
| # tokenizer = AutoTokenizer.from_pretrained("MilaNLProc/feel-it-italian-sentiment", cache_dir="data/") | |
| # model_sent = AutoModelForSequenceClassification.from_pretrained("MilaNLProc/feel-it-italian-sentiment", cache_dir="data/") | |
| # model_sent.eval() | |
| # tokenizer_emo = AutoTokenizer.from_pretrained("MilaNLProc/feel-it-italian-emotion", cache_dir="data/") | |
| # model_emo = AutoModelForSequenceClassification.from_pretrained("MilaNLProc/feel-it-italian-emotion", cache_dir="data/") | |
| # model_emo.eval() | |
| # if save_model_locally: | |
| # model_sent.save_pretrained('./local_models/sentiment_ITA') | |
| # tokenizer.save_pretrained('./local_models/sentiment_ITA') | |
| # model_emo.save_pretrained('./local_models/emotion_ITA') | |
| # tokenizer_emo.save_pretrained('./local_models/emotion_ITA') | |
| # else: | |
| # tokenizer = AutoTokenizer.from_pretrained("./local_models/sentiment_ITA/") | |
| # model_sent = AutoModelForSequenceClassification.from_pretrained("./local_models/sentiment_ITA/", num_labels=2) | |
| # model_sent.eval() | |
| # tokenizer_emo = AutoTokenizer.from_pretrained("./local_models/emotion_ITA/") | |
| # model_emo = AutoModelForSequenceClassification.from_pretrained("./local_models/emotion_ITA/", num_labels=4) | |
| # model_emo.eval() | |
| # #%%generator_sent | |
| from transformers import pipeline | |
| generator_sent = pipeline(task="text-classification", model='./local_models/sentiment_ITA/', top_k=None) | |
| generator_emo = pipeline(task="text-classification", model='./local_models/emotion_ITA/', top_k=None) | |
| def sentiment_emoji(input_abs): | |
| if(input_abs ==""): | |
| return "🤷♂️" | |
| res = generator_sent(input_abs)[0] | |
| print("res: ", res) | |
| res = {res[x]["label"]: res[x]["score"] for x in range(len(res))} | |
| res["🙂 positive"] = res.pop("positive") | |
| res["🙁 negative"] = res.pop("negative") | |
| return res | |
| def emotion_emoji(input_abs): | |
| if(input_abs ==""): | |
| return "🤷♂️" | |
| res = generator_emo(input_abs)[0] | |
| res = {res[x]["label"]: res[x]["score"] for x in range(len(res))} | |
| res["😃 joy"] = res.pop("joy") | |
| res["😡 anger"] = res.pop("anger") | |
| res["😨 fear"] = res.pop("fear") | |
| res["😟 sadness"] = res.pop("sadness") | |
| return res | |
| #%% | |
| import gradio as gr | |
| demo = gr.Blocks() | |
| with demo: | |
| gr.Markdown("# Analisi sentimento/emozioni del testo italiano") | |
| with gr.Row(): | |
| with gr.Column(): | |
| text_input = gr.Textbox(placeholder="Scrivi qui") | |
| button_1 = gr.Button("Invia") | |
| with gr.Column(): | |
| label_sem = gr.Label() | |
| label_emo = gr.Label() | |
| # gr.Interface(fn=emotion_emoji, inputs=text_input, outputs="label") | |
| button_1.click(sentiment_emoji, inputs=text_input, outputs=label_sem, api_name="sentiment") | |
| button_1.click(emotion_emoji, inputs=text_input, outputs=label_emo, api_name="emotion") | |
| demo.launch() | |
| print("Running is terminated") |